scholarly journals Revealing Hi-C subcompartments by imputing high-resolution inter-chromosomal chromatin interactions

2018 ◽  
Author(s):  
Kyle Xiong ◽  
Jian Ma
Keyword(s):  
High Resolution ◽  
Genome Organization ◽  
Cell Types ◽  
High Coverage ◽  
Chromatin Interactions ◽  
Gm12878 Cell ◽  
Additional Cell ◽  
Multiple Cell ◽  
Spatial Genome Organization ◽  
Different Cell Types

AbstractThe higher-order genome organization and its variation in different cellular conditions remains poorly understood. Recent high-resolution genome-wide mapping of chromatin interactions using Hi-C has revealed that chromosomes in the human genome are spatially segregated into distinct subcompartments. However, due to the requirement on sequencing coverage of the Hi-C data to define subcompartments, to date subcompartment annotation is only available in the GM12878 cell line, making it impractical to compare Hi-C subcompartment patterns across multiple cell types. Here we develop a new computational approach, named Sniper, based on an autoencoder and multilayer perceptron classifier to infer subcompartments using typical Hi-C datasets with moderate coverage. We demonstrated that Sniper can accurately reveal subcompartments based on Hi-C datasets with moderate coverage and can significantly outperform an existing method that uses numerous epigenomic datasets as input features in GM12878. We applied Sniper to eight additional cell lines to identify the variation of Hi-C subcompartments across different cell types. Sniper revealed that chromosomal regions with conserved and more dynamic subcompartment annotations across cell types have different patterns of functional genomic features. This work demonstrates that Sniper is effective in identifying subcompartments without the need of high-coverage Hi-C data and has the potential to provide new insights into the spatial genome organization variation across different cell types.

scholarly journals Revealing Hi-C subcompartments by imputing inter-chromosomal chromatin interactions

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Kyle Xiong ◽  
Jian Ma
Keyword(s):  
Genome Organization ◽  
Cell Types ◽  
Chromatin Interaction ◽  
High Coverage ◽  
Chromatin Interactions ◽  
Genome Wide ◽  
Gm12878 Cell ◽  
Additional Cell ◽  
Cell Type Specific ◽  
Spatial Genome Organization

Abstract Higher-order genome organization and its variation in different cellular conditions remain poorly understood. Recent high-coverage genome-wide chromatin interaction mapping using Hi-C has revealed spatial segregation of chromosomes in the human genome into distinct subcompartments. However, subcompartment annotation, which requires Hi-C data with high sequencing coverage, is currently only available in the GM12878 cell line, making it impractical to compare subcompartment patterns across cell types. Here we develop a computational approach, SNIPER (Subcompartment iNference using Imputed Probabilistic ExpRessions), based on denoising autoencoder and multilayer perceptron classifier to infer subcompartments using typical Hi-C datasets with moderate coverage. SNIPER accurately reveals subcompartments using moderate coverage Hi-C datasets and outperforms an existing method that uses epigenomic features in GM12878. We apply SNIPER to eight additional cell lines and find that chromosomal regions with conserved and cell-type specific subcompartment annotations have different patterns of functional genomic features. SNIPER enables the identification of subcompartments without high-coverage Hi-C data and provides insights into the function and mechanisms of spatial genome organization variation across cell types.

scholarly journals A systems perspective of heterocellular signaling

2018 ◽  
Vol 62 (4) ◽  
pp. 607-617 ◽  
Author(s):  
Alan Wells ◽  
H. Steven Wiley
Keyword(s):  
Cell Types ◽  
Regulatory Processes ◽  
Technical Developments ◽  
Building Models ◽  
Realistic Description ◽  
Multiple Cell ◽  
Solid Foundation ◽  
And Function ◽  
Multicellular Organisms ◽  
Different Cell Types

Signal exchange between different cell types is essential for development and function of multicellular organisms, and its dysregulation is causal in many diseases. Unfortunately, most cell-signaling work has employed single cell types grown under conditions unrelated to their native context. Recent technical developments have started to provide the tools needed to follow signaling between multiple cell types, but gaps in the information they provide have limited their usefulness in building realistic models of heterocellular signaling. Currently, only targeted assays have the necessary sensitivity, selectivity, and spatial resolution to usefully probe heterocellular signaling processes, but these are best used to test specific, mechanistic models. Decades of systems biology research with monocultures has provided a solid foundation for building models of heterocellular signaling, but current models lack a realistic description of regulated proteolysis and the feedback processes triggered within and between cells. Identification and understanding of key regulatory processes in the extracellular environment and of recursive signaling patterns between cells will be essential to building predictive models of heterocellular systems.

scholarly journals Freeform printing of heterotypic tumor models within cell-laden microgel matrices

2020 ◽  
Author(s):  
Thomas G. Molley ◽  
Gagan K. Jalandhra ◽  
Stephanie R. Nemec ◽  
Aleczandria S. Tiffany ◽  
Brendan A. C. Harley ◽  
...  
Keyword(s):  
Cell Types ◽  
Hierarchical Organization ◽  
Layer By Layer ◽  
Direct Writing ◽  
Tissue Microenvironment ◽  
Fundamental Research ◽  
Multiple Cell ◽  
Different Cell Types ◽  
Local Stiffness

AbstractThe tissue microenvironment is comprised of a complex assortment of multiple cell types, matrices, membranes and vessel structures. Emulating this complex and often hierarchical organization in vitro has proved a considerable challenge, typically involving segregation of different cell types using layer-by-layer printing or lithographically patterned microfluidic devices. Bioprinting in granular materials is a new methodology with tremendous potential for tissue fabrication. Here, we demonstrate the first example of a complex tumor microenvironment that combines direct writing of tumor aggregates, freeform vasculature channels, and a tunable macroporous matrix as a model to studying metastatic signaling. Our photocrosslinkable microgel suspensions yield local stiffness gradients between particles and the intervening space, while enabling the integration of virtually any cell type. Using computational fluid dynamics, we show that removal of a sacrificial Pluronic ink defines vessel-mimetic channel architectures for endothelial cell linings. Pairing this vasculature with 3D printing of melanoma aggregates, we find that tumor cells within proximity migrated into the prototype vasculature. Together, the integration of perfusable channels with multiple spatially defined cell types provides new avenues for modelling development and disease, with scope for fundamental research and drug development.

scholarly journals MUNIn (Multiple cell-type UNifying long-range chromatin Interaction detector): a statistical framework for identifying long-range chromatin interactions from multiple cell types

2020 ◽  
Author(s):  
Weifang Liu ◽  
Armen Abnousi ◽  
Qian Zhang ◽  
Yun Li ◽  
Ming Hu ◽  
...  
Keyword(s):  
Long Range ◽  
Spatial Organization ◽  
Critical Role ◽  
Cell Types ◽  
Chromatin Interaction ◽  
Cell Type ◽  
Statistical Framework ◽  
Chromatin Interactions ◽  
The Status ◽  
Multiple Cell

AbstractChromatin spatial organization (interactome) plays a critical role in genome function. Deep understanding of chromatin interactome can shed insights into transcriptional regulation mechanisms and human disease pathology. One essential task in the analysis of chromatin interactomic data is to identify long-range chromatin interactions. Existing approaches, such as HiCCUPS, FitHiC/FitHiC2 and FastHiC, are all designed for analyzing individual cell types. None of them accounts for unbalanced sequencing depths and heterogeneity among multiple cell types in a unified statistical framework. To fill in the gap, we have developed a novel statistical framework MUNIn (Multiple cell-type UNifying long-range chromatin Interaction detector) for identifying long-range chromatin interactions from multiple cell types. MUNIn adopts a hierarchical hidden Markov random field (H-HMRF) model, in which the status (peak or background) of each interacting chromatin loci pair depends not only on the status of loci pairs in its neighborhood region, but also on the status of the same loci pair in other cell types. To benchmark the performance of MUNIn, we performed comprehensive simulation studies and real data analysis, and showed that MUNIn can achieve much lower false positive rates for detecting cell-type-specific interactions (33.1 - 36.2%), and much enhanced statistical power for detecting shared peaks (up to 74.3%), compared to uni-cell-type analysis. Our data demonstrated that MUNIn is a useful tool for the integrative analysis of interactomic data from multiple cell types.

scholarly journals Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies

2018 ◽  
Author(s):  
Xiangyu Luo ◽  
Can Yang ◽  
Yingying Wei
Keyword(s):  
High Resolution ◽  
Statistical Method ◽  
Individual Cell ◽  
Association Studies ◽  
Cell Types ◽  
Cell Type ◽  
Cpg Sites ◽  
Cpg Site ◽  
Cell Type Specific ◽  
Different Cell Types

In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. Here, we propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types.

scholarly journals accuEnhancer: Accurate enhancer prediction by integration of multiple cell type data with deep learning

2020 ◽  
Author(s):  
Yi-An Tung ◽  
Wen-Tse Yang ◽  
Tsung-Ting Hsieh ◽  
Yu-Chuan Chang ◽  
June-Tai Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cell Types ◽  
Regulatory Elements ◽  
Sequence Motifs ◽  
Cell Type ◽  
Enhancer Activity ◽  
Multiple Cell ◽  
Type Data ◽  
Different Cell Types ◽  
Multiple Cell Type

AbstractEnhancers are one class of the regulatory elements that have been shown to act as key components to assist promoters in modulating the gene expression in living cells. At present, the number of enhancers as well as their activities in different cell types are still largely unclear. Previous studies have shown that enhancer activities are associated with various functional data, such as histone modifications, sequence motifs, and chromatin accessibilities. In this study, we utilized DNase data to build a deep learning model for predicting the H3K27ac peaks as the active enhancers in a target cell type. We propose joint training of multiple cell types to boost the model performance in predicting the enhancer activities of an unstudied cell type. The results demonstrated that by incorporating more datasets across different cell types, the complex regulatory patterns could be captured by deep learning models and the prediction accuracy can be largely improved. The analyses conducted in this study demonstrated that the cell type-specific enhancer activity can be predicted by joint learning of multiple cell type data using only DNase data and the primitive sequences as the input features. This reveals the importance of cross-cell type learning, and the constructed model can be applied to investigate potential active enhancers of a novel cell type which does not have the H3K27ac modification data yet.AvailabilityThe accuEnhancer package can be freely accessed at: https://github.com/callsobing/accuEnhancer

scholarly journals Predicting cell-to-cell communication networks using NATMI

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Rui Hou ◽  
Elena Denisenko ◽  
Huan Ting Ong ◽  
Jordan A. Ramilowski ◽  
Alistair R. R. Forrest
Keyword(s):  
Single Cell ◽  
Communication Networks ◽  
Cell Communication ◽  
Cost Effective ◽  
Cell Types ◽  
Sequencing Technologies ◽  
Multiple Cell ◽  
Multicellular Interactions ◽  
Autocrine Signalling ◽  
Different Cell Types

Abstract Development of high throughput single-cell sequencing technologies has made it cost-effective to profile thousands of cells from diverse samples containing multiple cell types. To study how these different cell types work together, here we develop NATMI (Network Analysis Toolkit for Multicellular Interactions). NATMI uses connectomeDB2020 (a database of 2293 manually curated ligand-receptor pairs with literature support) to predict and visualise cell-to-cell communication networks from single-cell (or bulk) expression data. Using multiple published single-cell datasets we demonstrate how NATMI can be used to identify (i) the cell-type pairs that are communicating the most (or most specifically) within a network, (ii) the most active (or specific) ligand-receptor pairs active within a network, (iii) putative highly-communicating cellular communities and (iv) differences in intercellular communication when profiling given cell types under different conditions. Furthermore, analysis of the Tabula Muris (organism-wide) atlas confirms our previous prediction that autocrine signalling is a major feature of cell-to-cell communication networks, while also revealing that hundreds of ligands and their cognate receptors are co-expressed in individual cells suggesting a substantial potential for self-signalling.

High-resolution SEM of kidney tissue using cryo-techniques

1990 ◽  
Vol 48 (3) ◽  
pp. 8-9
Author(s):  
P Walther ◽  
P Herter ◽  
J Hentschel ◽  
H Hentschel
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Room Temperature ◽  
Kidney Tissue ◽  
Cell Types ◽  
Electron Gun ◽  
Precise Localization ◽  
Transmission Electron ◽  
Different Cell Types ◽  
Scanning Electron

The kidney is a complex zonated organ with a variety of different cell types. For the study of the functional and morphological features, the precise localization in the zones is relevant, which requires the evaluation of rather large portions of tissue. Transmission electron microscopy of replicas of tissue is limited by difficulties to obtain sufficiently large specimens. In order to overcome this problem cryopreparation methods and high resolution field emission scanning electron microscopy (SEM) were used.1 mm3 cubes of perfusion fixed rabbit kidneys cryoprotected with glycerol were frozen by plunging into liquid propane. For further preparation two different methods were employed.1: Samples were fractured in liquid nitrogen with a scalpel, freeze substituted using methanol with glutaraldehyde and osmiumtetroxid, warmed to room temperature, critical point dried, and coated by electron gun evaporation with 2 nm of platinum at an angle of 45°, and 10 nm of carbon perpendicularly.

scholarly journals Vascular Calcification in Chronic Kidney Disease: Diversity in the Vessel Wall

Biomedicines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 404
Author(s):  
Prabhatchandra Dube ◽  
Armelle DeRiso ◽  
Mitra Patel ◽  
Dhanushya Battepati ◽  
Bella Khatib-Shahidi ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Vascular Calcification ◽  
Molecular Mechanisms ◽  
Cell Types ◽  
Complex Process ◽  
Cardiovascular Morbidity And Mortality ◽  
Depth Analysis ◽  
Multiple Cell ◽  
Different Cell Types

Vascular calcification (VC) is one of the major causes of cardiovascular morbidity and mortality in patients with chronic kidney disease (CKD). VC is a complex process expressing similarity to bone metabolism in onset and progression. VC in CKD is promoted by various factors not limited to hyperphosphatemia, Ca/Pi imbalance, uremic toxins, chronic inflammation, oxidative stress, and activation of multiple signaling pathways in different cell types, including vascular smooth muscle cells (VSMCs), macrophages, and endothelial cells. In the current review, we provide an in-depth analysis of the various kinds of VC, the clinical significance and available therapies, significant contributions from multiple cell types, and the associated cellular and molecular mechanisms for the VC process in the setting of CKD. Thus, we seek to highlight the key factors and cell types driving the pathology of VC in CKD in order to assist in the identification of preventative, diagnostic, and therapeutic strategies for patients burdened with this disease.

scholarly journals DeeReCT-TSS: A novel meta-learning-based method annotates TSS in multiple cell types based on DNA sequences and RNA-seq data

2021 ◽  
Author(s):  
Juexiao Zhou ◽  
Bin Zhang ◽  
Haoyang Li ◽  
Longxi Zhou ◽  
Zhongxiao Li ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Cell Types ◽  
Rna Seq ◽  
Sources Of Information ◽  
Genome Wide ◽  
A Genome ◽  
Meta Learning ◽  
Multiple Cell ◽  
Different Cell Types ◽  
Genome Scale

The accurate annotation of TSSs and their usage is critical for the mechanistic understanding of gene regulation under different biological contexts. To fulfill this, specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner. Various computational tools have also been developed for in silico prediction of TSSs solely based on genomic sequences. Most of these tools have drastic false positive predictions when applied on the genome-scale. Here, we present DeeReCT-TSS, a deep-learning-based method that is capable of TSSs identification across the whole genome based on DNA sequences and conventional RNA-seq data. We show that by effectively incorporating these two sources of information, DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types. Furthermore, we develop a meta-learning-based extension for simultaneous transcription start site (TSS) annotation on 10 cell types, which enables the identification of cell-type-specific TSS. Finally, we demonstrate the high precision of DeeReCT-TSS on two independent datasets from the ENCODE project by correlating our predicted TSSs with experimentally defined TSS chromatin states.

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